The existing systems of hand rehabilitation always design different rehabilitation medical apparatus and systems according to the patients’ needs. This kind of system always contain problems such as complexity, using only single training programs, inconvenient to wear and high cost. For these reasons, this paper uses gesture recognition technology and augmented reality technology to design a simple and interactive hand rehabilitation supplementary system. The system uses a low-cost, non-contact device named Leap Motion as the input device, and Unity3D as the development engine, realizing three functional modules: conventional training, AR game training and auxiliary functions. This rehabilitation training project with different levels of difficulty increases the fun and challenge of the rehabilitation process. Users can use the system to assist the treatment activity of hand rehabilitation anytime and anywhere. The system, which has good application value, can also be used in other physical rehabilitation fields.
via Gesture Interaction and Augmented Reality based Hand Rehabilitation Supplementary System – IEEE Conference Publication
Nowadays virtual reality (VR) technology give us the considerable opportunities to develop new methods to supplement traditional physiotherapy with sustain beneficial quantity and quality of rehabilitation. VR tools, like Leap motion have received great attention in the recent few years because of their immeasurable applications, whish include gaming, robotics, education, medicine etc. In this paper we present a game for hand rehabilitation using the Leap Motion controller. The main idea of gamification of hand rehabilitation is to help develop the muscle tonus and increase precision in gestures using the opportunities that VR offer by making the rehabilitation process more effective and motivating for patients.
Source: Gamification of Hand Rehabilitation Process Using Virtual Reality Tools: Using Leap Motion for Hand Rehabilitation – IEEE Xplore Document
This paper investigates Kinect device application during rehabilitation of people with an ischemic stroke. There are many similar application using Kinect as a tool during rehabilitation. This paper is focused on measurement of Kinect’s spatial accuracy and proposition of body states and exercises according to the Motor assessment scale for stroke (MAS). The system observes the whole rehabilitation process and objectively compares ranges of movement during each exercise. Angles between limbs are computed in the skeletal body joints projection to three anatomical planes, which enables a better insight to subject performance. The system is easily implemented with a consumer-grade computer and a low-cost Kinect device. Selected exercises are presented together with the angles evolution, body states recognition and the MAS Scale after the stroke classification.
Source: Kinect V2 as a tool for stroke recovery: Pilot study of motion scale monitoring – IEEE Xplore Document
We developed a tele-rehabilitation system to improve community rehabilitation for patients who are discharged early from hospital. The developed tele-rehabilitation system consists of devices designed to reduce the physical and economic burden on users while promoting optimum user movement. A Backend-as-a-Service cloud computing service was used for the communication between terminals. A non-contact sensor, Kinect, was used to measure movement. In addition, we used a three-dimensional (3D) display to present 3D images using binocular parallax, to encourage smooth movement of patients. We used this system for stroke patients and found improvements in task-performance time, smoothness of movements, and range of motion in all patients. No major issues occurred during the tele-rehabilitation. These results demonstrated the high operability and efficacy of our cloud service-based 3D virtual reality tele-rehabilitation system.
Source: IEEE Xplore Document – Trial operation of a cloud service-based three-dimensional virtual reality tele-rehabilitation system for stroke patients
Virtual reality therapy systems have the potential to increase the intensity and frequency of physical activity of stroke patients at home. This might help to increase the dose of rehabilitation, without the costs associated with clinic visits and therapist supervision.
We present a therapy game that continuously estimates the patient’s arm reachable three-dimensional (3D) workspace with a voxel-based model and selects targets to be reached accordingly, in order to increase challenge without causing frustration. This exercise is implemented on a novel, inertial measurement unit (IMU) based virtual reality system for the training of upper limb function. We present data from a pilot trial with 5 chronic stroke patients who trained for 6 weeks at home and without therapist supervision.
On average, the patients’ in-game assessed 3D workspace grew by 10.7% in volume and their score on the Fugl-Meyer Upper Extremity score improved by 5 points. The average self-selected therapy time, over the course of the therapy, was 16.8 h. These results suggest that the proposed assessment-driven target selection is viable for unsupervised home therapy and could form the basis for additional therapy games in the future.
Source: IEEE Xplore Abstract – Assessment-driven arm therapy at home using an IMU-based virtual reality system